Determining patterns as actionable information from sensors in buildings
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Solution Overview
Problem
Building managers face challenges in effectively utilizing real-time data from sensors to optimize HVAC operations, as the data is often overwhelming and unprocessed, leading to inefficient energy use and inaccurate control of indoor air quality.
Innovation Solution
A system comprising indoor air quality sensors and a controller that uses machine learning algorithms to identify patterns in sensor data, compare current readings to historical patterns, and adjust HVAC operations accordingly, reducing unnecessary switching and improving energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If real-time sensor data is provided to building managers, then access to operational information is improved, but the data becomes overwhelming and unhelpful when unprocessed
Solution Approach 1:
The patent introduces an intermediary processing layer between the sensors and building managers. This layer automatically analyzes sensor data, identifies patterns, and generates actionable insights, thereby mediating between the raw data stream and the user interface to prevent information overload while preserving all critical operational information.
Solution Approach 2:
The system enables self-service by automatically processing and analyzing sensor data without requiring manual intervention from building managers. The automated pattern recognition and anomaly detection algorithms independently evaluate the data streams, generating alerts and recommendations that are directly actionable by facility operators.
2Loss of energy
If HVAC equipment is operated on a fixed schedule, then energy conservation is improved, but control accuracy of indoor air quality deteriorates
Solution Approach 1:
The patent transitions from static, fixed-schedule HVAC operation to dynamic, real-time control based on actual sensor data. The system continuously monitors indoor air quality parameters and automatically adjusts HVAC operations in response to changing conditions, optimizing both energy consumption and air quality control accuracy through adaptive behavior.
Solution Approach 2:
The system implements closed-loop feedback control by continuously monitoring indoor air quality sensors and using this information to adjust HVAC operations. The feedback mechanism compares actual air quality conditions against target parameters and automatically modifies equipment operation to maintain optimal conditions, thereby improving both energy efficiency and control precision.
3Use of energy by moving object
If HVAC system switches on/off frequently to conserve energy, then energy efficiency is improved, but system reliability and comfort deteriorate due to unnecessary switching
Solution Approach 1:
The patent applies partial action by using pattern recognition to distinguish between transient fluctuations and genuine anomalies requiring HVAC intervention. Rather than responding to every sensor reading change, the system selectively activates HVAC adjustments only when patterns indicate genuine air quality issues, thereby reducing unnecessary switching while maintaining adequate response to actual problems.
Data Source
AI summary
A system and method for identifying patterns of indoor air quality sensors. A method includes receiving sensor data from a sensor to determine indoor air quality, the sensor including indoor air quality (IAQ) sensors and identifying a pattern of the sensor data for a period of time. The method may also include comparing current sensor data to the identified pattern of the sensor data and transmitting a message to a user device based at least in part on the comparison to indicate the indoor air quality and the pattern of the sensor data.